diff --git a/scripts/plotly_streamlit.py b/scripts/plotly_streamlit.py
index b42906d..4275cb5 100755
--- a/scripts/plotly_streamlit.py
+++ b/scripts/plotly_streamlit.py
@@ -34,22 +34,22 @@ st.markdown("""
@st.cache(hash_funcs={Connection: id})
-def get_connection(path:str):
- return sqlite3.connect(path,check_same_thread=False)
+def get_connection(path: str):
+ return sqlite3.connect(path, check_same_thread=False)
def get_data(conn: Connection):
- df1=pd.read_sql("SELECT * FROM detections", con=conn)
+ df1 = pd.read_sql("SELECT * FROM detections", con=conn)
return df1
+
conn = get_connection(URI_SQLITE_DB)
# Read in the cereal data
# df = load_data()
-df=get_data(conn)
-df2=df.copy()
-df2['DateTime']=pd.to_datetime(df2['Date'] + " " + df2['Time'])
-df2=df2.set_index('DateTime')
-
+df = get_data(conn)
+df2 = df.copy()
+df2['DateTime'] = pd.to_datetime(df2['Date'] + " " + df2['Time'])
+df2 = df2.set_index('DateTime')
# Filter on date range
@@ -59,115 +59,117 @@ df2=df2.set_index('DateTime')
# Date as slider
Start_Date = pd.to_datetime(df2.index.min()).date()
-End_Date = pd.to_datetime(df2.index.max()).date()
+End_Date = pd.to_datetime(df2.index.max()).date()
Date_Slider = st.slider('Date Range',
- min_value = Start_Date-timedelta(days=1),
- max_value = End_Date,
- value=(Start_Date,
- End_Date)
- )
+ min_value=Start_Date - timedelta(days=1),
+ max_value=End_Date,
+ value=(Start_Date,
+ End_Date)
+ )
-
-filt = (df2.index >= pd.Timestamp(Date_Slider[0])) & (df2.index <= pd.Timestamp(Date_Slider[1]+timedelta(days=1)))
+filt = (df2.index >= pd.Timestamp(Date_Slider[0])) & (df2.index <= pd.Timestamp(Date_Slider[1] + timedelta(days=1)))
df2 = df2[filt]
-#Create species count for selected date range
+# Create species count for selected date range
-Specie_Count=df2['Com_Name'].value_counts()
+Specie_Count = df2['Com_Name'].value_counts()
-#Create species treemap
+# Create species treemap
# Create Hourly Crosstab
-hourly=pd.crosstab(df2['Com_Name'],df2.index.hour, dropna=False)
+hourly = pd.crosstab(df2['Com_Name'], df2.index.hour, dropna=False)
# Filter on species
species = list(hourly.index)
-cols1,cols2= st.columns((1,1))
+cols1, cols2 = st.columns((1, 1))
top_N = cols1.slider(
'Select Number of Birds to Show',
- min_value = 1,
- value=min(10,len(Specie_Count))
- )
+ min_value=1,
+ value=min(10, len(Specie_Count))
+)
top_N_species = (df2['Com_Name'].value_counts()[:top_N])
-specie = cols2.selectbox('Which bird would you like to explore for the dates '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'?', species,
- index=species.index(list(top_N_species.index)[0]))
+specie = cols2.selectbox('Which bird would you like to explore for the dates ' + str(Date_Slider[0]) + ' to ' + str(Date_Slider[1]) + '?', species,
+ index=species.index(list(top_N_species.index)[0]))
-font_size=15
+font_size = 15
-#specie filter
-filt=df2['Com_Name']==specie
+# specie filter
+filt = df2['Com_Name'] == specie
-df_counts=df2[filt].resample('D').count()
+df_counts = df2[filt].resample('D').count()
fig = make_subplots(
- rows=3, cols =2,
- specs= [[{"type":"xy","rowspan":3}, {"type":"polar","rowspan":2}], [{"rowspan":1}, {"rowspan":1} ], [None, {"type":"xy","rowspan":1}]],
- subplot_titles=('Top '+ str(top_N) + ' Species in Date Range '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'',
- 'Total Detect:'+str('{:,}'.format(sum(df_counts.Time)))+
- ' Confidence Max:'+str('{:.2f}%'.format(max(df2[df2['Com_Name']==specie]['Confidence'])*100))+
- ' '+' Median:'+str('{:.2f}%'.format(np.median(df2[df2['Com_Name']==specie]['Confidence'])*100))
- )
+ rows=3, cols=2,
+ specs=[[{"type": "xy", "rowspan": 3}, {"type": "polar", "rowspan": 2}], [
+ {"rowspan": 1}, {"rowspan": 1}], [None, {"type": "xy", "rowspan": 1}]],
+ subplot_titles=('Top ' + str(top_N) + ' Species in Date Range ' + str(Date_Slider[0]) + ' to ' + str(Date_Slider[1]) + '',
+ 'Total Detect:' + str('{:,}'.format(sum(df_counts.Time))) +
+ ' Confidence Max:' + str('{:.2f}%'.format(max(df2[df2['Com_Name'] == specie]['Confidence']) * 100)) +
+ ' ' + ' Median:' +
+ str('{:.2f}%'.format(np.median(df2[df2['Com_Name'] == specie]['Confidence']) * 100))
)
-fig.layout.annotations[1].update(x=0.7,y=0.25, font_size=15)
+)
+fig.layout.annotations[1].update(x=0.7, y=0.25, font_size=15)
-#Plot seen species for selected date range and number of species
-fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1,col=1)
+# Plot seen species for selected date range and number of species
+fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1, col=1)
fig.update_layout(
margin=dict(l=0, r=0, t=50, b=0),
- yaxis={'categoryorder':'total ascending'})
+ yaxis={'categoryorder': 'total ascending'})
# Set 360 degrees, 24 hours for polar plot
theta = np.linspace(0.0, 360, 24, endpoint=False)
-d=pd.DataFrame(np.zeros((23,1))).squeeze()
+d = pd.DataFrame(np.zeros((23, 1))).squeeze()
detections = hourly.loc[specie]
-detections=(d+detections).fillna(0)
-fig.add_trace(go.Barpolar(r = detections, theta=theta), row=1, col=2)
+detections = (d + detections).fillna(0)
+fig.add_trace(go.Barpolar(r=detections, theta=theta), row=1, col=2)
fig.update_layout(
autosize=False,
- width = 1000,
- height = 500,
+ width=1000,
+ height=500,
showlegend=False,
- polar = dict(
- radialaxis = dict(
- tickfont_size = font_size,
- showticklabels = True,
- hoverformat = "#%{theta}:
Popularity: %{percent} %{r}"
- ),
- angularaxis = dict(
- tickfont_size= font_size,
- rotation = -90,
- direction = 'clockwise',
+ polar=dict(
+ radialaxis=dict(
+ tickfont_size=font_size,
+ showticklabels=True,
+ hoverformat="#%{theta}:
Popularity: %{percent} %{r}"
+ ),
+ angularaxis=dict(
+ tickfont_size=font_size,
+ rotation=-90,
+ direction='clockwise',
tickmode='array',
- tickvals=[0,15,35,45,60,75,90,105,120,135,150,165,180,195,210,225,240,255,270,285,300,315,330,345],
- ticktext=['12am','1am','2am','3am','4am','5am', '6am','7am','8am','9am','10am','11am','12pm','1pm','2pm','3pm','4pm','5pm', '6pm','7pm','8pm','9pm','10pm','11pm'],
- hoverformat = "#%{theta}:
Popularity: %{percent} %{r}"
+ tickvals=[0, 15, 35, 45, 60, 75, 90, 105, 120, 135, 150, 165,
+ 180, 195, 210, 225, 240, 255, 270, 285, 300, 315, 330, 345],
+ ticktext=['12am', '1am', '2am', '3am', '4am', '5am', '6am', '7am', '8am', '9am', '10am', '11am',
+ '12pm', '1pm', '2pm', '3pm', '4pm', '5pm', '6pm', '7pm', '8pm', '9pm', '10pm', '11pm'],
+ hoverformat="#%{theta}:
Popularity: %{percent} %{r}"
),
- ),
- )
+ ),
+)
-
-daily=pd.crosstab(df2['Com_Name'],df2.index.date, dropna=False)
+daily = pd.crosstab(df2['Com_Name'], df2.index.date, dropna=False)
fig.add_trace(go.Bar(x=daily.columns, y=daily.loc[specie]), row=3, col=2)
# container=st.container()
# config={'displayModelBar': False}
-st.plotly_chart(fig, use_container_width=True) #, config=config)
+st.plotly_chart(fig, use_container_width=True) # , config=config)
# cols3,cols4=st.columns((1,1))
-#
+#
# extract_date=Date_Slider
-#
+#
# audio_file = open('/home/*/BirdSongs/Extracted/By_Date/2022-03-22/Yellow-streaked_Greenbul/Yellow-streaked_Greenbul-77-2022-03-22-birdnet-15:04:28.mp3', 'rb')
# audio_bytes = audio_file.read()
# cols4.audio(audio_bytes, format='audio/mp3')